Articles tagged with Computational Science
A new AI method can distinguish between the overall sounds of healthy and unhealthy coral reefs, making it a valuable tool for monitoring reef health. The technique uses machine learning to analyze sound recordings and track the progress of reef restoration projects.
Researchers at the University of Innsbruck have successfully implemented a universal set of gates on encoded logical quantum bits, enabling fault-tolerant quantum computing. The demonstration showcases two essential gates: CNOT and T-gates, which are crucial for programming all algorithms.
A new project led by University of Illinois professor Jingrui He seeks to detect and predict insider threats in large organizations. The team will use multimodal data to identify outliers and rare category types of insider threats and propose dynamic update techniques.
A new computer-aided diagnostic tool, Deep-Lung Parenchyma-Enhancing (DLPE), uses artificial intelligence to reveal signs of pulmonary fibrosis in COVID long-haulers, helping to explain respiratory symptoms and improve disease management.
A new machine-learning framework has been developed to improve the design of catalysts, which speed up chemical reactions. The approach analyzes the conversion of carbon monoxide to methanol using a copper-based catalyst and identifies key steps that need to be tweaked to increase productivity.
Researchers developed a technique to study supermassive black holes smaller than M87's by measuring the brightness of their shadows over time. The 'shadow' signal can reveal the size and shape of a black hole's event horizon, shedding light on gravity's nature.
A new study uses artificial intelligence to predict bone fractures in cancer patients by creating a digital twin of the vertebra. The AI-assisted framework, ReconGAN, simulates how tumors affect the spine and predicts fracture risks, offering medical experts better treatment strategies and patient decisions.
The Texas Advanced Computing Center (TACC) has selected 21 scientific codes and 'grand challenge'-class science problems that will receive funding through the Characteristic Science Applications program. The program aims to improve scientific software, generate benchmarks for the Leadership-Class Computing Facility, and demonstrate the...
Scientists at the University of Oxford have developed an 'optomemristor' device that facilitates three-factor learning and emulation of biological computations, making it possible to perform complex machine learning tasks. The device uses both light and electrical signals to interact and consume very little energy.
Researchers developed a machine-learning method that allows robots to pick up and place never-before-seen objects in random poses, requiring only 10 human demonstrations. The system uses a neural network specifically designed to reconstruct 3D shapes, enabling the robot to generalize to new object orientations.
The Taylor Geospatial Institute brings together eight leading research institutions to collaborate on geospatial technology research and development. The Institute aims to accelerate St. Louis' position as the global center of geospatial innovation, with a focus on key areas such as food security, geospatial health, and national security.
A study by Tokyo University of Science researchers has demonstrated that a computationally-light model can simulate complex brain cell responses, including periodic and quasi-periodic responses. The Izhikevich neuron model was found to be capable of reproducing both types of responses at lower computational cost.
Researchers fill in gaps in Human Reference Genome, discovering repetitive sections are a major source of human variation and genetic diversity. The Telomere-2-Telomere project reveals complex architectural features with significant consequences for understanding human evolution and biological function.
Seismologists have developed methods to take wave signals from seismometers and reverse engineer features of the medium they pass through, known as seismic tomography. A new full-waveform inversion model uses 3D wave simulations and data sensitivities at the global scale to improve the resolution of current seismic models.
Researchers at RIKEN Center for Computational Science used computer simulations to show that extreme weather phenomena can be controlled by making small adjustments to variables in the weather system. The study's findings promise multiple applications, including preventing and mitigating extreme windstorms.
The Human Brain Project (HBP) has brought together neuroscientists from different disciplines to work collaboratively on common goals. The HBP researchers outline their scientific approach and illustrate the potential of EBRAINS infrastructure for neuroscience research.
Historical climate model data can now be improved using super-resolution technology, a new analysis tool that enhances older meteorological model data. Researchers have successfully reconstructed high-quality and high-resolution data using this method, which was previously used for image and video upscaling.
The African BioGenome Project aims to sequence the genomes of Africa's endemic plants, animals, fungi, and protists. This will build resilience in breeding, sustainable food systems, and biodiversity conservation, aligning with the post-2020 Global Biodiversity Framework and the United Nations Sustainable Development Goals.
Researchers at Rice University developed a new program called Variabel to accurately identify 'low-frequency' variants of the virus that causes COVID-19. By distinguishing true variants from sequencing errors, Variabel enables rapid characterization of within-host variation, which could aid in discovering future mutations.
A new MIT study found that lawyers' frequent use of long definitions in sentences makes legal texts difficult for non-lawyers to understand. Center-embedding, a common feature of legal writing, was the biggest contributor to comprehension difficulty. The researchers suggest reducing center-embedding and simplifying jargon to improve ac...
Researchers at University of Texas at Austin create first-ever biologically authentic computer model of HIV-1 virus liposome, shedding light on replication and infectivity. The study reveals key characteristics of the liposome's asymmetry and its role in shaping macroscopic properties.
GIST researchers propose a new strategy for crime prevention using artificial intelligence, trained on a large-scale dataset of deviant incident reports and corresponding images. The model, called DevianceNet, can accurately classify and detect deviant places, making it a useful tool in urban safety development.
The University of Texas at El Paso establishes a new Systems Modeling and Simulation concentration, enhancing students' skills in data analytics, computer simulation, and machine learning. The program aims to train students for innovative industries with rapidly changing environments.
Researchers studied how diverse neural network training datasets impact generalization. They found that data diversity is key to overcoming bias, but also degrade performance when neural networks are trained for multiple tasks simultaneously. The study highlights the importance of designing diverse and controlled datasets in machine le...
Researchers from Argonne National Laboratory have created a set of new practices to guide the curation of high energy physics datasets, making them more FAIR and reusable. The goal is to automate the finding and use of data for humans and streamline the development of AI tools for scientific discovery.
Anastasios Kyrillidis has won a National Science Foundation CAREER Award to explore the theory and design of non-convex optimization algorithms. His research aims to devise algorithmic foundations and theory that will accelerate problem-solving in machine learning, information processing, and optimization.
A team of scientists has developed a pioneering approach to combine advances in computer vision with ecological expertise to analyze wildlife populations. By leveraging AI and machine learning algorithms, researchers can extract key features from images and videos to quickly classify species, count individuals, and track behavior.
Researchers developed an algorithm that incorporates customer behavior into recommendation systems, making more accurate and personalized suggestions. The technique, known as tensor decomposition, analyzes data in multiple dimensions to capture complex patterns and relationships.
A new simulation suggests that energy released near a black hole's event horizon during magnetic field line reconnection powers the intense flares. The process involves interactions between the magnetic field and material falling into the black hole, releasing hot plasma particles that radiate away as photons.
A team of researchers from Ritsumeikan University developed an unprecedented stream cipher using chaos theory to create highly secure cryptographic systems. The new system is resistant to statistical attacks and eavesdropping, even against quantum computers, making it a promising solution for post-quantum era cryptosystems.
Nathan Dautenhahn, a Rice University computer scientist, has received a prestigious CAREER Award to develop 'Least-Authority Virtual Architecture' (LAVA) strategy for retrofitting existing products with meaningful firewalling. This approach aims to systematically analyze and optimize security boundaries in complex systems.
Rice University scientists employ machine-learning techniques to streamline the process of synthesizing graphene from waste through flash Joule heating. The lab used its custom optimization model to improve graphene crystallization from four starting materials over 173 trials.
A team of scientists at Brookhaven National Laboratory has identified a molecule with significant potential to disable the COVID-19 virus. The molecule was discovered using high-throughput virtual screening and laboratory experiments, and its ability to bind to the virus's main protease was confirmed through structural studies.
Researchers at the University of Oklahoma have developed a molecular framework that solves the challenge of predicting peptide structures. The framework bridges experimental and computer sciences, enabling the use of machine learning and artificial intelligence to model peptide structures for materials engineering.
Researchers from KTU proposed a deep-learning-based method for 3D human shape reconstruction using limited-angle depth data. The method can be integrated with existing virtual reality tools and has potential applications in telemedicine and remote diagnostics.
A team of NTU Singapore scientists has developed a predictive computer programme using wearable technology data to detect depression risk. The programme, Ycogni model, achieves an accuracy of 80% in detecting individuals at high or low risk of depression.
A study by the University of Texas at Austin found that software development teams given greater autonomy are more productive and have higher customer satisfaction rates. The researchers tested 461 projects over 50 months and found a 39% increase in value added for autonomous teams compared to traditional teams.
Professor Alexander Ecker is awarded a Starting Grant to develop machine-learning methods to describe neurons' shape and function, leveraging a large dataset from the US Brain Initiative. The research aims to uncover how a neuron's shape relates to its role in sensory information processing.
A new model suggests that cell-to-cell communication plays a crucial role in determining cell fate, particularly in the development of blood cell types. This finding has significant implications for understanding cancer development and identifying leukemia cells of origin.
Researchers developed a fair-diverse allocation optimization framework to optimize the distribution of COVID-19 vaccines, treatments, and testing supplies. The model aims to get limited resources to the most vulnerable subgroups while ensuring fairness and diversity, independent of demographic background.
A study by researchers at the University of São Paulo developed a model that can predict the likelihood of politicians being convicted of corruption based on their voting histories. The model achieved 90% accuracy in identifying corrupt deputies.
The ATIQ project aims to develop reliable, user-friendly quantum computing demonstrators based on ion trap technology within 30 months. The consortium will optimize hardware for applications in chemistry and finance, paving the way for new approaches in credit risk assessment.
Researchers at the University of Missouri have developed a free online resource that speeds up data analysis of human genomes three times faster than current methods. This enables scientists to see how an individual's genome makes them susceptible to different diseases in different ways, ultimately reducing associated costs and increas...
A team of scientists has created a neural network that can predict and generate new protein structures using deep learning. The network, trained on random protein sequences, can produce stable protein shapes with remarkable accuracy.
A study proposes a new conceptualization framework to characterize music in terms of emotions, enabling better-adapted models for people's characteristics. The research aims to combat the problem of subjectivity and cultural differences in emotion recognition.
A new 'image analysis pipeline' called TDAExplore gives scientists rapid insight into how cells are changed by disease, using a combination of microscopy, topology, and artificial intelligence. This approach can provide objective information on cell changes, such as the movement of proteins like actin, even with limited training data.
Johns Hopkins Medicine researchers have developed a 3D map of blood vessels and stem cells in a mouse skull, revealing previously unknown niches for stem cell residence. The map provides precise locations of blood vessels and stem cells, which could be used to repair wounds and generate new bone tissue.
A new study found that tumors with one mutant copy of the PIK3CA gene tend to have lower PI3K activity, while those with two or more copies often have higher PIK3α activity, leading to more aggressive tumors and poorer prognosis. The research also discovered a counterintuitive relationship between PI3K mutations, PI3K activity, and ste...
A new coil design could mitigate disruption-driven runaway electrons in tokamaks. The SPARC team's innovative coil structure addresses the threat by introducing a non-axisymmetric perturbation that spoils confinement and protects the machine.
Researchers from academia and industry will converge at Lehigh University to discuss innovative solutions for optimizing efficiency and resiliency in the global supply chain. The workshop aims to leverage machine learning for prescriptive analytics, enabling proactive optimization of supply chain operations.
Researchers used computer simulations to predict the presence of hydroxyl radicals, which clean pollutants from the atmosphere. The study showed that traditional models had widely varying forecasts due to uncertainties in gas emissions, and that better models can aid in combating climate change.
A study found that search engines like Yandex and Google often display inaccurate information about health treatments, including false claims about remedy effectiveness. The researchers argue that clearer warnings about possible health risks are needed for medical queries.
Researchers at University of Copenhagen have developed a new quantum circuit that can operate and measure all four qubits simultaneously. This breakthrough resolves a significant engineering headache in the development of large functional quantum computers.
Researchers found that future goals are represented by a pattern of neural activity resembling previous visits, and this activity can re-emerge upon decision to target a location. The orbitofrontal cortex plays a key role in representing future goals during navigation.
A pilot study examines graph-theoretical properties of brain networks in traumatic brain injury and controls, showing association with balance impairment and structural damage. The study uses EEG-based functional connectivity measures during a balance perturbation task to explore underlying neural mechanisms.
Assistant Professor Kang Hao Cheong and his team discovered that chaotic switching for quantum coin Parrondo's games has similar underlying ideas to encryption. They found that using pre-generated chaotic sequences enhances the work, making it easier to invert the encrypted message to obtain the original state.
Researchers at the Salk Institute combined genomic and epidemiological data to reveal that some widespread beliefs about cancer-causing genes are incorrect. For example, KRAS is found to be involved in only about 11% of all cancers, not 25% as previously thought. This study could help guide genetic research for more effective treatments.
A team of researchers from the University of Illinois Urbana-Champaign used advanced machine learning to model the physico-chemical properties of a molten salt compound called FLiNaK, enabling accurate atomic-scale reproduction and prediction of behavior under specific reactor conditions. This computational framework can help character...
The Ohio State University has received a $15 million NSF grant to create the Imageomics Institute, which will use machine learning methodologies to extract biological traits from images. This new approach, called imageomics, aims to transform biomedical, agricultural and basic biological sciences.
Researchers have developed an AI-powered platform that allows scientists to grow virtual tumors and optimize nanoparticle designs using artificial intelligence. The new EVONANO platform has the potential to improve targeted cancer treatments, enabling personalized therapies for individual patients.